Avalanche modeling is an essential tool to assess snow avalanche hazard. Today, most popular numerical approaches adopt depth-averaged equations. These methods are computationally efficient but limited in capturing processes occurring in the flow depth direction, e.g. erosion or deposition, which are often considered using ad hoc parameterizations or neglected completely. However, processes such as snow erosion, can crucially influence flow dynamics and run-out and are often not negligible. We address these issues by using a new three-dimensional (3-D) model, based on the material point method and finite strain elastoplasticity. To assess the possibilities and challenges associated with these highly detailed but computationally expensive calculations, we simulated the ‘Salezer’ snow avalanche that released in Davos, Switzerland in 2019. To reproduce the event in our simulations, we use the release areas mapped in a photogrammetric drone survey and estimate the snow conditions on the day of the event. We compare macroscopic features, such as flow outline and snow deposition of the simulated avalanche to field observations. An in-depth analysis of transient 3-D flow structures at the avalanche head not only demonstrates the degree of physical detail in the model, but also highlights challenges which still need to be addressed.

For snow-avalanche hazard mapping, one needs efficient tools that nevertheless capture the essential physical processes. The code MoT-PSA (Method of Transport – Powder Snow Avalanche) described here is based on the two-layer depth-averaged formulation for mixed snow avalanches developed by Eglit and co-workers in the 1980s but is extended to 3-D terrain and uses a fast numerical scheme based on the method of transport. Compared to previous works, we introduce novel formulations for the suspension and deposition of snow from the dense core. Snow cover and air entrainment are quantified with physics-based models. A sensitivity study of the model parameters on an idealized topography shows that both the dense core and the parameters of the powder snow cloud (PSC) governing particle suspension and settling significantly affect the dynamics. As expected, we observe that snow cover entrainment favours the formation of large PSCs with long runout. The powder-snow avalanche that occurred in Lom (Norway) on 27 February 2020 is back-calculated using MoT-PSA. With plausible parameter values, the model reproduces the dense core stopping at the gully's base and the dilute PSC travelling across the frozen lake for almost 1 km.

In the course of an artificially triggered avalanche, a particle tracking procedure is combined with supplementary measurements, including Global Navigation Satellite System (GNSS) positioning, terrestrial laser scanning and Doppler radar measurements. Specifically, an intertial measurement unit is mounted inside a rigid sphere, which is placed in the avalanche track. The sphere is entrained by the moving snow, recording translational accelerations, angular velocities and the flux density of Earth's magnetic field. Based on the recorded data, we present a threefold analysis: (i) a qualitative data interpretation, identifying different particle motion phases which are associated with corresponding flow regimes, (ii) a quantitative time integration algorithm, determining the corresponding particle trajectory and associated velocities on the basis of standard sensor calibration, and (iii) an improved quantitative evaluation relying on a novel in situ sensor calibration technique, which is motivated by the limitations of the given dataset. The final results, i.e. the evolution of the angular orientation of the sensor unit, translational and rotational velocities and estimates of the sensor trajectory, are assessed with respect to their reliability and relevance for avalanche dynamics as well as for future design of experiments.

Spatial variability of snowpack properties adds uncertainty in the evaluation of avalanche hazard. We propose a combined mechanical–statistical approach to study how spatial variation of slab depth affects the skier-triggering probability and possible release size. First, we generate multiple slab depth maps on a plane fictional slope based on Gaussian Random Fields (GRF) for a specific set of mean, variance and correlation length. For each GRF, we derive analytically the Skier Propagation Index (SPI). We then simulate multiple skier tracks and computed the probability based on the number of skier hits where SPI is below 1. Finally, we use a depth-averaged material point method to evaluate the possible avalanche size for given slab depth variations. The results of this analysis show that large correlation lengths and small variances lead to a lower probability of skier-triggering as it reduces the size and the number of areas with low slab depth. Then, we show the effect of skiing style and skier group size on skier-triggering probability. Spatial variability also affects the possible avalanche size by adding stress fluctuation causing early or late tensile failure. Finally, we demonstrate with our models the well-known relationship between the probability and the size in avalanche forecasting.

The relationship between the behavior of water in snow and its microstructure is crucial to improve the prediction of wet snow disasters. X-ray computed tomography (X-ray CT) is frequently used to observe snow microscopically. However, distinguishing between ice and water in the X-ray images is difficult because ice exhibits an X-ray absorption coefficient similar to that of water. In contrast, magnetic resonance imaging (MRI) acquires nuclear magnetic resonance (NMR) signals of protons in a liquid and visualizes the NMR signal intensity, enabling discrimination between water and ice signals. However, snow grains and pore spaces cannot be distinguished in MRI images because they do not generate NMR signals.

To investigate the relationship between the microstructure of snow and the distribution of liquids in snow, we developed a novel method that combines X-ray CT and MRI images to compensate for the disadvantages associated with each method. Using this method, we successfully visualized where liquid (C12H24) occupied pore spaces. We also showed the possibility of using C12H24 instead of water to obtain water retention curve of snow cover, which is a fundamental aspect of hydraulic properties. Although there is room for improvement in the visualization of water in snow, such as shortening the imaging time to escape snow metamorphosis and image superimposition methods, this method is expected to effectively elucidate the behavior of water in snow and clarify the characteristics of wet snow.

We developed a Handheld Integrating Sphere Snow Grain Sizer (HISSGraS) for field use to measure the specific surface area (SSA) of snow. In addition to snow samples, HISSGraS can directly measure snow surfaces and snow pit walls. The basic measurement principle is the same as that of the IceCube SSA instrument. The retrieval algorithm for SSA from reflectance employs two conversion equations formulated using spherical and nonspherical grain shape models. We observed SSAs using HISSGraS, IceCube and the gas adsorption method in a snowfield in Hokkaido, Japan. Intercomparison of the results confirmed that with HISSGraS direct measurement, SSA profile observations can be completed in just ~1/10 the time required for measurement of snow samples. Our results also suggest that HISSGraS and IceCube have similar accuracy when the same snow samples are measured using the same grain shape model. However, SSAs of near-surface snow layers measured using the three techniques exhibited some biases, possibly due to rapid snow metamorphism or melting during measurement and some technical issues with optical techniques. When excluding SSA data for the surface layer, which metamorphosed remarkably during measurement, IceCube- and HISSGraS-derived SSAs correlated strongly with those obtained by gas adsorption and HISSGraS accuracy is 21–34%.

Liquid water at the ground–snow interface is thought to play a crucial role in the release of glide-snow avalanches, which can be massive and threaten infrastructure in alpine regions. Several mechanisms have been postulated to explain the formation of this interfacial water. However, these mechanisms remain poorly understood, in part because suitable measurement techniques are lacking. Here, we demonstrate the use of neutron radiography for imaging water transport in soil–snow systems. Columns of sand, gravel and snow were used to simulate the capillary forces of the soil–vegetation–snow layering found in nature. The columns were connected to a water reservoir to maintain a constant-pressure boundary condition and placed in a climatic chamber within the neutron beam. We show that neutron radiography is capable of measuring changes in the optical density distribution (related to liquid water content) within all three layers of the model system. Results suggest that a porous interface between the sand and snow may induce the formation of a water layer in the basal snowpack. Improved understanding of the water transport in soil–snow systems should lead to better prediction of glide-snow avalanche release and could also benefit other fields such as snow hydrology.

Understanding the dynamics of snow avalanches is crucial for predicting their destructive potential and mobility. To gain insight into avalanche dynamics at a particle level, the AvaNode in-flow sensor system was developed. These synthetic particles, equipped with advanced and affordable sensors such as an inertial measurement unit (IMU) and global navigation satellite system (GNSS), travel with the avalanche flow. This study focuses on assessing the feasibility of the in-flow measurement systems. The experiments were conducted during the winter seasons of 2021–2023, both in static snow cover and dynamic avalanche conditions of medium-sized events. Radar measurements were used in conjunction with the particle trajectories and velocities to understand the behaviour of the entire avalanche flow. The dynamic avalanche experiments allowed to identify three distinct particle flow states: (I) initial rapid acceleration, (II) a steady state flow with the highest velocities (9–17 ms−1), and (III) a longer deceleration state accompanied by the largest measured rotation rates. The particles tend to travel towards the tail of the avalanche and reach lower velocities compared to the frontal approach velocities deduced from radar measurements (ranging between 23–28 ms−1). The presented data give a first insight in avalanche particle measurements.

The spatiotemporal distribution of snow affects hydrological and climatological processes at different scales. Accordingly, quantifying geometric features of snow-cover patterns is important, providing a valuable complement for snow water equivalent (SWE) modelling. This study on satellite-based morphological analysis originally uses two types of geometric indexes: (1) MN, Minkowski numbers (area (MN1), perimeter (MN2), Euler number (MN3)), and (2) CL, average chord length, to describe the morphology of Sentinel-2-derived snow-covered areas (SCAs), within the high-alpine site Zugspitze for a 5 year period. Results indicate that they capture the seasonal variability of snow-cover patterns, particularly during accumulation and ablation. Being to some degree independent from each other, MN2, MN3 and CL provide additional information upon shape, connectivity and length scale of snow cover, compared to most used indexes (e.g. fractional SCA). Correlation values up to +0.7 for MN2, +0.58 for MN3 and +0.46 for CL were observed with selected topographic characteristics, suggesting a close connection between geometric features of snow cover and ground features. Comparing in situ SWE measurements with MN and CL shows a correlation between −0.5 and +0.5. These indexes can hence be applied in combination with in situ data and/or modelling approaches to improve spatially distributed SWE in high-alpine catchments.

Glide-snow avalanches release due to a loss of friction at the snow–ground interface, which can result in large avalanches that endanger infrastructure in alpine regions. The underlying processes are still relatively poorly understood, in part due to the limited data available on glide processes. Here, we introduce a pixel-based algorithm to detect glide cracks in time-lapse photographs under changing illumination and shadow conditions. The algorithm was applied to 14 years of time-lapse images at Dorfberg (Davos, Switzerland). We analysed 947 glide-snow events at a high-spatial (0.5 m) and temporal (2–15 min) resolution. Avalanche activity and glide-crack opening dynamics were investigated across timescales ranging from seasonally to hourly. Events were separated into surface (meltwater percolation) and interface events (no meltwater percolation). The results show that glide activity is highly variable between and within seasons. Most avalanches released without crack formation or within 24 h after crack opening, and release was favoured in the afternoon hours. Glide rates often showed a stick–slip behaviour. The acceleration of glide rates and non-constant increases in glide crack aspect ratio were indicators for avalanche release. This comprehensive dataset provides the basis for further investigations into glide-snow avalanche drivers.

Snow cover on sea ice poses a challenge for radar measurements as microwave penetration into snow is not yet fully understood. In this study, the aim is to investigate microwave penetration into snow on Arctic sea ice using commercial C (6 GHz) and K (26 GHz) band tank radars. Nadir-looking radar measurements collected at nine study locations over first-year and multiyear landfast sea ice in the Lincoln Sea in May 2018 are analysed together with detailed measurements of the physical properties of the snow cover to determine the dominant scattering horizons at both frequencies. They are evaluated for the feasibility to determine snow depth. The results show that in 39% of the measurements and only on first-year ice a major fraction of the C band radar backscatter originated closer to the snow–ice interface potentially enabling snow depth retrieval. At K band, 81% of the radar returns originated from the snow surface. Partly confirming the findings of previous studies, however, the analysis was potentially hampered by relatively warm air temperatures (up to $-0.9^\circ$C) during the study period as well as stratigraphic features and inconclusive microwave interaction with the saline basal layers found in the snow cover on first-year ice.

Dry-snow slab avalanches release due to crack propagation in a weak snow layer under a cohesive snow slab. Crack propagation speeds can provide insights into the potential size of avalanches and inform fracture and avalanche release models. Despite their importance, slope-scale crack speed measurements from real avalanches are limited. Further, most existing slope-scale measurements utilize the appearance of slab fractures on the snow surface. However, we have no evidence that the appearance of surface cracking is a good indicator of the weak layer crack propagation tip. Here we present a novel method to estimate crack propagation speed from snow surface movements in avalanche videos. Our technique uses changes in frame pixel intensity, allowing us to detect the location of weak layer cracks well before slab fractures appear on the snow surface. We use field experiments and numerical simulations to validate our method before applying it to five avalanches. Our estimates show that cracks propagate faster up and down the slope than in the cross-slope direction; this suggests that different propagation regimes likely govern crack propagation up/down the slope, cross-slope and in flat terrain.